most citedCollocation-based Robust Variational Physics-Informed Neural Networks (CRVPINN)

1 citations · 1 across the 4 of their papers we have counts for

collaborators
Showing cs.LGShow all

2 papers · 1 filter

cs.LG2026

Python library supporting Discrete Variational Formulations and training solutions with Collocation-based Robust Variational Physics Informed Neural Networks (DVF-CRVPINN)

Tomasz Służalec, Marcin Łoś, Askold Vilkha +1

We explore the possibility of solving Partial Differential Equations (PDEs) using discrete weak formulations. We propose a programming environment for defining a discrete computati…

cs.LG2024★ 1 cited

Collocation-based Robust Variational Physics-Informed Neural Networks (CRVPINN)

Marcin Łoś, Tomasz Służalec, Paweł Maczuga +3

Physics-Informed Neural Networks (PINNs) have been successfully applied to solve Partial Differential Equations (PDEs). Their loss function is founded on a strong residual minimiza…